পদ্ধতির তুলনা করুন

	ওয়েটেড এক্সপোনেনশিয়াল র‍্যান্ডম গ্রাফ মডেল	ওয়েটেড ডিগ্রি সেন্ট্রালিটি
ক্ষেত্র	নেটওয়ার্ক বিশ্লেষণ	নেটওয়ার্ক বিশ্লেষণ
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2012	2004
প্রবর্তক	Krivitsky, P. N.	Barrat, A.; Barthélemy, M.; Pastor-Satorras, R.; Vespignani, A.
ধরন	Statistical network model	Centrality measure for weighted networks
মৌলিক উৎস	Krivitsky, P. N. (2012). Exponential-family random graph models for valued networks. Electronic Journal of Statistics, 6, 1100–1128.	Barrat, A., Barthélemy, M., Pastor-Satorras, R., & Vespignani, A. (2004). The architecture of complex weighted networks. Proceedings of the National Academy of Sciences, 101(11), 3747–3752.
অপর নাম	W-ERGM, valued ERGM, weighted p-star model, valued exponential random graph model	node strength, strength centrality, weighted node degree, WDC
সম্পর্কিত	4	6
সারসংক্ষেপ	The Weighted Exponential Random Graph Model (W-ERGM) extends the classic binary ERGM framework to networks whose edges carry quantitative values — such as frequency of contact, trade volume, or collaboration intensity. It models the entire valued-edge network as a probability distribution defined over all possible weighted graphs, enabling researchers to test whether structural patterns such as reciprocity, transitivity, or degree distribution arise beyond what chance alone would produce.	Weighted degree centrality — also called node strength — extends the classic degree centrality measure to networks whose edges carry numeric weights. Instead of simply counting a node's connections, it sums the weights of all edges incident to that node, capturing both the volume and the intensity of a node's ties in a single, interpretable score.